Overview

Dataset statistics

Number of variables30
Number of observations10526
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory240.0 B

Variable types

Numeric20
Categorical10

Alerts

State has constant value "NY" Constant
Station has a high cardinality: 340 distinct values High cardinality
Date has a high cardinality: 2107 distinct values High cardinality
Unnamed: 0 is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
longitude is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
ZipCode is highly correlated with Wdspeed_Max and 1 other fieldsHigh correlation
Temperature_Max is highly correlated with Temperature_Avg and 4 other fieldsHigh correlation
Temperature_Avg is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Temperature_M is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_Max is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_Avg is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_M is highly correlated with Temperature_Max and 5 other fieldsHigh correlation
Humidity_Max is highly correlated with Humidity_Avg and 2 other fieldsHigh correlation
Humidity_Avg is highly correlated with Dewpot_M and 3 other fieldsHigh correlation
Humidity_M is highly correlated with Humidity_Max and 1 other fieldsHigh correlation
Wdspeed_Max is highly correlated with ZipCode and 1 other fieldsHigh correlation
Wdspeed_Avg is highly correlated with ZipCode and 1 other fieldsHigh correlation
Pressure_Max is highly correlated with Pressure_MHigh correlation
Pressure_M is highly correlated with Pressure_MaxHigh correlation
Precipitation_Total is highly correlated with Humidity_Max and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
longitude is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
ZipCode is highly correlated with Wdspeed_Max and 1 other fieldsHigh correlation
Temperature_Max is highly correlated with Temperature_Avg and 4 other fieldsHigh correlation
Temperature_Avg is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Temperature_M is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_Max is highly correlated with Temperature_Max and 5 other fieldsHigh correlation
Dewpot_Avg is highly correlated with Temperature_Max and 6 other fieldsHigh correlation
Dewpot_M is highly correlated with Temperature_Max and 5 other fieldsHigh correlation
Humidity_Max is highly correlated with Dewpot_Max and 3 other fieldsHigh correlation
Humidity_Avg is highly correlated with Dewpot_Avg and 3 other fieldsHigh correlation
Humidity_M is highly correlated with Humidity_Max and 1 other fieldsHigh correlation
Wdspeed_Max is highly correlated with ZipCode and 1 other fieldsHigh correlation
Wdspeed_Avg is highly correlated with longitude and 2 other fieldsHigh correlation
Pressure_Max is highly correlated with Pressure_MHigh correlation
Pressure_M is highly correlated with Pressure_MaxHigh correlation
Unnamed: 0 is highly correlated with latitudeHigh correlation
latitude is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
longitude is highly correlated with latitudeHigh correlation
ZipCode is highly correlated with Wdspeed_Max and 1 other fieldsHigh correlation
Temperature_Max is highly correlated with Temperature_Avg and 4 other fieldsHigh correlation
Temperature_Avg is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Temperature_M is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_Max is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_Avg is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Dewpot_M is highly correlated with Temperature_Max and 4 other fieldsHigh correlation
Humidity_Max is highly correlated with Humidity_AvgHigh correlation
Humidity_Avg is highly correlated with Humidity_Max and 1 other fieldsHigh correlation
Humidity_M is highly correlated with Humidity_AvgHigh correlation
Wdspeed_Max is highly correlated with ZipCode and 1 other fieldsHigh correlation
Wdspeed_Avg is highly correlated with ZipCode and 1 other fieldsHigh correlation
Pressure_Max is highly correlated with Pressure_MHigh correlation
Pressure_M is highly correlated with Pressure_MaxHigh correlation
Weather_Type is highly correlated with StateHigh correlation
longitude is highly correlated with Borough and 3 other fieldsHigh correlation
Level is highly correlated with StateHigh correlation
Borough is highly correlated with longitude and 3 other fieldsHigh correlation
City is highly correlated with longitude and 3 other fieldsHigh correlation
latitude is highly correlated with longitude and 3 other fieldsHigh correlation
State is highly correlated with Weather_Type and 6 other fieldsHigh correlation
Month is highly correlated with StateHigh correlation
Unnamed: 0 is highly correlated with latitude and 7 other fieldsHigh correlation
latitude is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
longitude is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Borough is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
City is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
ZipCode is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Temperature_Max is highly correlated with Temperature_Avg and 7 other fieldsHigh correlation
Temperature_Avg is highly correlated with Temperature_Max and 7 other fieldsHigh correlation
Temperature_M is highly correlated with Temperature_Max and 7 other fieldsHigh correlation
Dewpot_Max is highly correlated with Temperature_Max and 9 other fieldsHigh correlation
Dewpot_Avg is highly correlated with Temperature_Max and 9 other fieldsHigh correlation
Dewpot_M is highly correlated with Temperature_Max and 10 other fieldsHigh correlation
Humidity_Max is highly correlated with Dewpot_Max and 4 other fieldsHigh correlation
Humidity_Avg is highly correlated with Dewpot_Max and 4 other fieldsHigh correlation
Humidity_M is highly correlated with Dewpot_M and 2 other fieldsHigh correlation
Wdspeed_Max is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Wdspeed_Avg is highly correlated with Unnamed: 0 and 7 other fieldsHigh correlation
Wdspeed_M is highly correlated with Wdspeed_AvgHigh correlation
Pressure_Max is highly correlated with ZipCodeHigh correlation
Level is highly correlated with Temperature_Max and 8 other fieldsHigh correlation
Weather_Type is highly correlated with Wdspeed_Max and 1 other fieldsHigh correlation
Year is highly correlated with Unnamed: 0High correlation
Month is highly correlated with Temperature_Max and 7 other fieldsHigh correlation
Month_Number is highly correlated with Temperature_Max and 7 other fieldsHigh correlation
Pressure_M is highly skewed (γ1 = -34.41313394) Skewed
Unnamed: 0 is uniformly distributed Uniform
latitude is uniformly distributed Uniform
longitude is uniformly distributed Uniform
Borough is uniformly distributed Uniform
City is uniformly distributed Uniform
Date is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Wdspeed_Avg has 505 (4.8%) zeros Zeros
Wdspeed_M has 9827 (93.4%) zeros Zeros
Precipitation_Total has 6528 (62.0%) zeros Zeros

Reproduction

Analysis started2021-12-03 01:42:56.494623
Analysis finished2021-12-03 01:43:49.907132
Duration53.41 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10526
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5262.5
Minimum0
Maximum10525
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:49.981144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile526.25
Q12631.25
median5262.5
Q37893.75
95-th percentile9998.75
Maximum10525
Range10525
Interquartile range (IQR)5262.5

Descriptive statistics

Standard deviation3038.738801
Coefficient of variation (CV)0.5774325512
Kurtosis-1.2
Mean5262.5
Median Absolute Deviation (MAD)2631.5
Skewness0
Sum55393075
Variance9233933.5
MonotonicityStrictly increasing
2021-12-02T20:43:50.103158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
6371
 
< 0.1%
47351
 
< 0.1%
88331
 
< 0.1%
26921
 
< 0.1%
6451
 
< 0.1%
67901
 
< 0.1%
47431
 
< 0.1%
88411
 
< 0.1%
27001
 
< 0.1%
Other values (10516)10516
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
105251
< 0.1%
105241
< 0.1%
105231
< 0.1%
105221
< 0.1%
105211
< 0.1%
105201
< 0.1%
105191
< 0.1%
105181
< 0.1%
105171
< 0.1%
105161
< 0.1%

Station
Categorical

HIGH CARDINALITY

Distinct340
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
KNYNEWYO103
2107 
KNYJACKS2
2106 
KNYNEWYO343
2106 
KNYBROOK54
2100 
KNYBRONX14
1772 
Other values (335)
335 

Length

Max length11
Median length10
Mean length10.22392172
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique335 ?
Unique (%)3.2%

Sample

1st rowKNYBRONX14
2nd rowKNYBRONX14
3rd rowKNYBRONX14
4th rowKNYBRONX14
5th rowKNYBRONX14

Common Values

ValueCountFrequency (%)
KNYNEWYO1032107
20.0%
KNYJACKS22106
20.0%
KNYNEWYO3432106
20.0%
KNYBROOK542100
20.0%
KNYBRONX141772
16.8%
KNYBRONX991
 
< 0.1%
KNYBRONX1701
 
< 0.1%
KNYBRONX3411
 
< 0.1%
KNYBRONX301
 
< 0.1%
KNYBRONX2831
 
< 0.1%
Other values (330)330
 
3.1%

Length

2021-12-02T20:43:50.227186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
knynewyo1032107
20.0%
knynewyo3432106
20.0%
knyjacks22106
20.0%
knybrook542100
20.0%
knybronx141772
16.8%
knybronx2781
 
< 0.1%
knybronx2761
 
< 0.1%
knybronx1181
 
< 0.1%
knybronx351
 
< 0.1%
knybronx201
 
< 0.1%
Other values (330)330
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
40.8616
2107 
40.5674
2107 
40.7638
2106 
40.7557
2106 
40.6215
2100 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40.8616
2nd row40.8616
3rd row40.8616
4th row40.8616
5th row40.8616

Common Values

ValueCountFrequency (%)
40.86162107
20.0%
40.56742107
20.0%
40.76382106
20.0%
40.75572106
20.0%
40.62152100
20.0%

Length

2021-12-02T20:43:50.329829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:50.396845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
40.86162107
20.0%
40.56742107
20.0%
40.76382106
20.0%
40.75572106
20.0%
40.62152100
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

longitude
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
-73.8809
2107 
-74.1343
2107 
-73.8831
2106 
-73.9918
2106 
-74.0096
2100 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-73.8809
2nd row-73.8809
3rd row-73.8809
4th row-73.8809
5th row-73.8809

Common Values

ValueCountFrequency (%)
-73.88092107
20.0%
-74.13432107
20.0%
-73.88312106
20.0%
-73.99182106
20.0%
-74.00962100
20.0%

Length

2021-12-02T20:43:50.501882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:50.569902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
73.88092107
20.0%
74.13432107
20.0%
73.88312106
20.0%
73.99182106
20.0%
74.00962100
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Borough
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Bronx
2107 
Staten Island
2107 
Manhattan
2106 
Queens
2106 
Brooklyn
2100 

Length

Max length13
Median length8
Mean length8.200266008
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBronx
2nd rowBronx
3rd rowBronx
4th rowBronx
5th rowBronx

Common Values

ValueCountFrequency (%)
Bronx2107
20.0%
Staten Island2107
20.0%
Manhattan2106
20.0%
Queens2106
20.0%
Brooklyn2100
20.0%

Length

2021-12-02T20:43:50.695930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:50.784933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
bronx2107
16.7%
staten2107
16.7%
island2107
16.7%
manhattan2106
16.7%
queens2106
16.7%
brooklyn2100
16.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

City
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Richmondtown
2107 
Botanical_Garden
2107 
Jackson_Heights
2106 
New_York
2106 
Dyker_Heights
2100 

Length

Max length16
Median length13
Mean length12.800114
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBotanical_Garden
2nd rowBotanical_Garden
3rd rowBotanical_Garden
4th rowBotanical_Garden
5th rowBotanical_Garden

Common Values

ValueCountFrequency (%)
Richmondtown2107
20.0%
Botanical_Garden2107
20.0%
Jackson_Heights2106
20.0%
New_York2106
20.0%
Dyker_Heights2100
20.0%

Length

2021-12-02T20:43:50.915963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:51.003000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
richmondtown2107
20.0%
botanical_garden2107
20.0%
jackson_heights2106
20.0%
new_york2106
20.0%
dyker_heights2100
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
NY
10526 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowNY
3rd rowNY
4th rowNY
5th rowNY

Common Values

ValueCountFrequency (%)
NY10526
100.0%

Length

2021-12-02T20:43:51.117026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:51.181029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
ny10526
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ZipCode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10676.08265
Minimum10018
Maximum11372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:51.230297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10018
5-th percentile10018
Q110306
median10458
Q311228
95-th percentile11372
Maximum11372
Range1354
Interquartile range (IQR)922

Descriptive statistics

Standard deviation530.3205836
Coefficient of variation (CV)0.04967370531
Kurtosis-1.645131201
Mean10676.08265
Median Absolute Deviation (MAD)440
Skewness0.2129488085
Sum112376446
Variance281239.9213
MonotonicityNot monotonic
2021-12-02T20:43:51.329069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
104582107
20.0%
113722106
20.0%
100182106
20.0%
112282100
20.0%
103061828
17.4%
10308279
 
2.7%
ValueCountFrequency (%)
100182106
20.0%
103061828
17.4%
10308279
 
2.7%
104582107
20.0%
112282100
20.0%
113722106
20.0%
ValueCountFrequency (%)
113722106
20.0%
112282100
20.0%
104582107
20.0%
10308279
 
2.7%
103061828
17.4%
100182106
20.0%

Temperature_Max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct786
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.48357401
Minimum12.9
Maximum101.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:51.453090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.9
5-th percentile35.1
Q149.1
median65.4
Q380.6
95-th percentile91.6
Maximum101.2
Range88.3
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.39402198
Coefficient of variation (CV)0.2852512793
Kurtosis-1.011335499
Mean64.48357401
Median Absolute Deviation (MAD)15.7
Skewness-0.166850897
Sum678754.1
Variance338.3400447
MonotonicityNot monotonic
2021-12-02T20:43:51.692155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8152
 
0.5%
72.944
 
0.4%
8244
 
0.4%
82.843
 
0.4%
88.342
 
0.4%
6142
 
0.4%
4642
 
0.4%
85.541
 
0.4%
77.539
 
0.4%
49.639
 
0.4%
Other values (776)10098
95.9%
ValueCountFrequency (%)
12.91
 
< 0.1%
13.31
 
< 0.1%
13.81
 
< 0.1%
141
 
< 0.1%
14.21
 
< 0.1%
14.41
 
< 0.1%
15.31
 
< 0.1%
15.43
< 0.1%
15.71
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
101.21
< 0.1%
1011
< 0.1%
100.41
< 0.1%
100.31
< 0.1%
1001
< 0.1%
99.81
< 0.1%
99.52
< 0.1%
99.31
< 0.1%
99.22
< 0.1%
99.12
< 0.1%

Temperature_Avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct762
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.92463424
Minimum7.3
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:51.814172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile29.525
Q142.6
median57
Q372.5
95-th percentile81.8
Maximum92
Range84.7
Interquartile range (IQR)29.9

Descriptive statistics

Standard deviation17.18509978
Coefficient of variation (CV)0.3018921423
Kurtosis-1.003970493
Mean56.92463424
Median Absolute Deviation (MAD)15
Skewness-0.152249123
Sum599188.7
Variance295.3276544
MonotonicityNot monotonic
2021-12-02T20:43:51.919202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.540
 
0.4%
76.237
 
0.4%
71.734
 
0.3%
42.133
 
0.3%
49.133
 
0.3%
74.633
 
0.3%
73.533
 
0.3%
72.532
 
0.3%
40.631
 
0.3%
76.431
 
0.3%
Other values (752)10189
96.8%
ValueCountFrequency (%)
7.31
< 0.1%
8.42
< 0.1%
8.61
< 0.1%
8.81
< 0.1%
9.31
< 0.1%
9.41
< 0.1%
9.61
< 0.1%
9.71
< 0.1%
9.81
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
922
< 0.1%
91.81
< 0.1%
91.71
< 0.1%
91.51
< 0.1%
91.41
< 0.1%
90.91
< 0.1%
90.81
< 0.1%
90.21
< 0.1%
89.81
< 0.1%
89.61
< 0.1%

Temperature_M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct740
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.07352271
Minimum-2.3
Maximum85.5
Zeros2
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size82.4 KiB
2021-12-02T20:43:52.033216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.3
5-th percentile23.4
Q136.7
median49.6
Q365.5
95-th percentile74.5
Maximum85.5
Range87.8
Interquartile range (IQR)28.8

Descriptive statistics

Standard deviation16.80266434
Coefficient of variation (CV)0.3355598614
Kurtosis-0.9492629577
Mean50.07352271
Median Absolute Deviation (MAD)14.3
Skewness-0.1465675579
Sum527073.9
Variance282.3295288
MonotonicityNot monotonic
2021-12-02T20:43:52.145241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7061
 
0.6%
70.555
 
0.5%
71.253
 
0.5%
69.150
 
0.5%
3749
 
0.5%
72.345
 
0.4%
69.345
 
0.4%
7345
 
0.4%
70.344
 
0.4%
36.143
 
0.4%
Other values (730)10036
95.3%
ValueCountFrequency (%)
-2.31
 
< 0.1%
-0.61
 
< 0.1%
02
< 0.1%
0.91
 
< 0.1%
1.61
 
< 0.1%
1.71
 
< 0.1%
21
 
< 0.1%
2.81
 
< 0.1%
3.41
 
< 0.1%
3.64
< 0.1%
ValueCountFrequency (%)
85.51
 
< 0.1%
84.22
< 0.1%
841
 
< 0.1%
83.12
< 0.1%
82.92
< 0.1%
82.81
 
< 0.1%
82.71
 
< 0.1%
82.63
< 0.1%
82.43
< 0.1%
82.11
 
< 0.1%

Dewpot_Max
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct785
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.73123694
Minimum-14.1
Maximum87.8
Zeros1
Zeros (%)< 0.1%
Negative18
Negative (%)0.2%
Memory size82.4 KiB
2021-12-02T20:43:52.263266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-14.1
5-th percentile20.6
Q137
median52.5
Q366.5
95-th percentile74.8
Maximum87.8
Range101.9
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.74254548
Coefficient of variation (CV)0.3497361104
Kurtosis-0.6962826341
Mean50.73123694
Median Absolute Deviation (MAD)14.6
Skewness-0.3990233271
Sum533997
Variance314.7979201
MonotonicityNot monotonic
2021-12-02T20:43:52.375291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.652
 
0.5%
68.451
 
0.5%
7349
 
0.5%
7048
 
0.5%
72.347
 
0.4%
68.747
 
0.4%
71.247
 
0.4%
71.146
 
0.4%
71.846
 
0.4%
6846
 
0.4%
Other values (775)10047
95.4%
ValueCountFrequency (%)
-14.11
< 0.1%
-11.41
< 0.1%
-9.41
< 0.1%
-6.11
< 0.1%
-61
< 0.1%
-5.31
< 0.1%
-4.91
< 0.1%
-4.41
< 0.1%
-3.51
< 0.1%
-3.21
< 0.1%
ValueCountFrequency (%)
87.81
< 0.1%
85.81
< 0.1%
85.11
< 0.1%
841
< 0.1%
83.81
< 0.1%
82.62
< 0.1%
82.41
< 0.1%
82.21
< 0.1%
81.12
< 0.1%
811
< 0.1%

Dewpot_Avg
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct811
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.31865856
Minimum-18.8
Maximum78.5
Zeros1
Zeros (%)< 0.1%
Negative74
Negative (%)0.7%
Memory size82.4 KiB
2021-12-02T20:43:52.491336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-18.8
5-th percentile12.6
Q129.7
median45.2
Q360.6
95-th percentile70.9
Maximum78.5
Range97.3
Interquartile range (IQR)30.9

Descriptive statistics

Standard deviation18.68745886
Coefficient of variation (CV)0.4216612026
Kurtosis-0.812673607
Mean44.31865856
Median Absolute Deviation (MAD)15.5
Skewness-0.2955740649
Sum466498.2
Variance349.2211188
MonotonicityNot monotonic
2021-12-02T20:43:52.612359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.230
 
0.3%
66.830
 
0.3%
63.730
 
0.3%
5130
 
0.3%
54.630
 
0.3%
6629
 
0.3%
68.929
 
0.3%
61.528
 
0.3%
29.528
 
0.3%
65.728
 
0.3%
Other values (801)10234
97.2%
ValueCountFrequency (%)
-18.81
< 0.1%
-16.21
< 0.1%
-13.91
< 0.1%
-121
< 0.1%
-11.51
< 0.1%
-11.41
< 0.1%
-11.11
< 0.1%
-10.91
< 0.1%
-9.71
< 0.1%
-9.22
< 0.1%
ValueCountFrequency (%)
78.51
 
< 0.1%
77.71
 
< 0.1%
76.51
 
< 0.1%
76.41
 
< 0.1%
76.21
 
< 0.1%
76.11
 
< 0.1%
762
< 0.1%
75.93
< 0.1%
75.81
 
< 0.1%
75.72
< 0.1%

Dewpot_M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct836
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.77145164
Minimum-26.7
Maximum76.1
Zeros5
Zeros (%)< 0.1%
Negative253
Negative (%)2.4%
Memory size82.4 KiB
2021-12-02T20:43:52.733380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-26.7
5-th percentile4.8
Q122.3
median38.7
Q354.6
95-th percentile67.3
Maximum76.1
Range102.8
Interquartile range (IQR)32.3

Descriptive statistics

Standard deviation19.82155834
Coefficient of variation (CV)0.5247761861
Kurtosis-0.8816216024
Mean37.77145164
Median Absolute Deviation (MAD)16.2
Skewness-0.2039915341
Sum397582.3
Variance392.8941749
MonotonicityNot monotonic
2021-12-02T20:43:52.855407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.941
 
0.4%
3641
 
0.4%
2839
 
0.4%
28.637
 
0.4%
3936
 
0.3%
43.236
 
0.3%
52.735
 
0.3%
6435
 
0.3%
21.935
 
0.3%
49.534
 
0.3%
Other values (826)10157
96.5%
ValueCountFrequency (%)
-26.71
< 0.1%
-21.31
< 0.1%
-19.91
< 0.1%
-17.81
< 0.1%
-17.51
< 0.1%
-172
< 0.1%
-16.81
< 0.1%
-16.41
< 0.1%
-15.92
< 0.1%
-15.71
< 0.1%
ValueCountFrequency (%)
76.11
 
< 0.1%
75.21
 
< 0.1%
73.62
< 0.1%
73.51
 
< 0.1%
73.42
< 0.1%
73.32
< 0.1%
73.21
 
< 0.1%
731
 
< 0.1%
72.93
< 0.1%
72.73
< 0.1%

Humidity_Max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.31730952
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:52.976446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile56
Q173
median86
Q394
95-th percentile98
Maximum100
Range73
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.81246704
Coefficient of variation (CV)0.1677954141
Kurtosis-0.03736134946
Mean82.31730952
Median Absolute Deviation (MAD)9
Skewness-0.8710962735
Sum866472
Variance190.7842457
MonotonicityNot monotonic
2021-12-02T20:43:53.087471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96580
 
5.5%
95576
 
5.5%
94562
 
5.3%
93482
 
4.6%
97439
 
4.2%
92430
 
4.1%
91339
 
3.2%
90337
 
3.2%
89309
 
2.9%
87291
 
2.8%
Other values (61)6181
58.7%
ValueCountFrequency (%)
271
 
< 0.1%
311
 
< 0.1%
321
 
< 0.1%
332
 
< 0.1%
343
 
< 0.1%
354
 
< 0.1%
366
0.1%
376
0.1%
385
 
< 0.1%
3913
0.1%
ValueCountFrequency (%)
100276
2.6%
99146
 
1.4%
98192
 
1.8%
97439
4.2%
96580
5.5%
95576
5.5%
94562
5.3%
93482
4.6%
92430
4.1%
91339
3.2%

Humidity_Avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct204
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.4831845
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:53.216481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q154
median66
Q377
95-th percentile90
Maximum100
Range80
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.44131117
Coefficient of variation (CV)0.235805746
Kurtosis-0.6616235914
Mean65.4831845
Median Absolute Deviation (MAD)12
Skewness-0.1091385299
Sum689276
Variance238.4340906
MonotonicityNot monotonic
2021-12-02T20:43:53.461544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63265
 
2.5%
66256
 
2.4%
74246
 
2.3%
64241
 
2.3%
69238
 
2.3%
67234
 
2.2%
65234
 
2.2%
73234
 
2.2%
71231
 
2.2%
61230
 
2.2%
Other values (194)8117
77.1%
ValueCountFrequency (%)
201
 
< 0.1%
222
 
< 0.1%
231
 
< 0.1%
245
 
< 0.1%
257
0.1%
25.91
 
< 0.1%
263
 
< 0.1%
277
0.1%
2817
0.2%
28.11
 
< 0.1%
ValueCountFrequency (%)
1005
 
< 0.1%
992
 
< 0.1%
9817
 
0.2%
9727
 
0.3%
9650
0.5%
9555
0.5%
9466
0.6%
9392
0.9%
9299
0.9%
91100
1.0%

Humidity_M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.94765343
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:53.596568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q135
median45
Q357
95-th percentile78
Maximum100
Range100
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.67258594
Coefficient of variation (CV)0.355131401
Kurtosis-0.2071939124
Mean46.94765343
Median Absolute Deviation (MAD)11
Skewness0.484087566
Sum494171
Variance277.9751218
MonotonicityNot monotonic
2021-12-02T20:43:53.716609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38281
 
2.7%
42272
 
2.6%
40268
 
2.5%
34264
 
2.5%
36262
 
2.5%
44257
 
2.4%
41257
 
2.4%
43252
 
2.4%
35249
 
2.4%
33246
 
2.3%
Other values (85)7918
75.2%
ValueCountFrequency (%)
01
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
103
 
< 0.1%
116
 
0.1%
129
0.1%
1315
0.1%
1412
0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
991
 
< 0.1%
971
 
< 0.1%
966
 
0.1%
9512
0.1%
9417
0.2%
9314
0.1%
9219
0.2%
9119
0.2%
9014
0.1%

Wdspeed_Max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct372
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.3733707
Minimum0
Maximum104.5
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:53.835622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16.9
median14
Q319.9
95-th percentile30.6
Maximum104.5
Range104.5
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2534788
Coefficient of variation (CV)0.6437932335
Kurtosis2.120204109
Mean14.3733707
Median Absolute Deviation (MAD)6
Skewness0.8998572237
Sum151294.1
Variance85.62686991
MonotonicityNot monotonic
2021-12-02T20:43:53.955662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17401
 
3.8%
13385
 
3.7%
15341
 
3.2%
11320
 
3.0%
19283
 
2.7%
21267
 
2.5%
2.9238
 
2.3%
12227
 
2.2%
14227
 
2.2%
2.5226
 
2.1%
Other values (362)7611
72.3%
ValueCountFrequency (%)
015
 
0.1%
0.212
 
0.1%
0.427
 
0.3%
0.73
 
< 0.1%
0.938
 
0.4%
11
 
< 0.1%
1.165
0.6%
1.383
0.8%
1.627
 
0.3%
1.8124
1.2%
ValueCountFrequency (%)
104.51
< 0.1%
76.11
< 0.1%
74.31
< 0.1%
72.91
< 0.1%
70.31
< 0.1%
66.91
< 0.1%
641
< 0.1%
61.11
< 0.1%
60.21
< 0.1%
59.51
< 0.1%

Wdspeed_Avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct186
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.403638609
Minimum0
Maximum28
Zeros505
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:54.072689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.6
median2.5
Q35.3
95-th percentile9.9
Maximum28
Range28
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.323169804
Coefficient of variation (CV)0.9763580054
Kurtosis2.047171492
Mean3.403638609
Median Absolute Deviation (MAD)2.1
Skewness1.306131893
Sum35826.7
Variance11.04345754
MonotonicityNot monotonic
2021-12-02T20:43:54.175712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1814
 
7.7%
0505
 
4.8%
0.2501
 
4.8%
0.3311
 
3.0%
0.4274
 
2.6%
0.5216
 
2.1%
0.6200
 
1.9%
0.7188
 
1.8%
1.9163
 
1.5%
0.8163
 
1.5%
Other values (176)7191
68.3%
ValueCountFrequency (%)
0505
4.8%
0.1814
7.7%
0.2501
4.8%
0.3311
 
3.0%
0.4274
 
2.6%
0.5216
 
2.1%
0.6200
 
1.9%
0.7188
 
1.8%
0.8163
 
1.5%
0.9129
 
1.2%
ValueCountFrequency (%)
281
< 0.1%
232
< 0.1%
22.61
< 0.1%
221
< 0.1%
21.91
< 0.1%
20.31
< 0.1%
19.91
< 0.1%
19.51
< 0.1%
191
< 0.1%
18.61
< 0.1%

Wdspeed_M
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct35
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1550826525
Minimum0
Maximum20
Zeros9827
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:54.287724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.9
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8400194025
Coefficient of variation (CV)5.416591663
Kurtosis132.5330524
Mean0.1550826525
Median Absolute Deviation (MAD)0
Skewness9.737210037
Sum1632.4
Variance0.7056325965
MonotonicityNot monotonic
2021-12-02T20:43:54.399749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
09827
93.4%
0.9204
 
1.9%
2146
 
1.4%
1101
 
1.0%
344
 
0.4%
2.932
 
0.3%
624
 
0.2%
1.319
 
0.2%
1.117
 
0.2%
716
 
0.2%
Other values (25)96
 
0.9%
ValueCountFrequency (%)
09827
93.4%
0.9204
 
1.9%
1101
 
1.0%
1.117
 
0.2%
1.319
 
0.2%
1.610
 
0.1%
1.89
 
0.1%
2146
 
1.4%
2.23
 
< 0.1%
2.52
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
171
 
< 0.1%
161
 
< 0.1%
14.81
 
< 0.1%
143
< 0.1%
131
 
< 0.1%
123
< 0.1%
103
< 0.1%
92
< 0.1%
8.11
 
< 0.1%

Pressure_Max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct182
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.07711381
Minimum28.82
Maximum32.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:54.555785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum28.82
5-th percentile29.56
Q129.94
median30.09
Q330.24
95-th percentile30.48
Maximum32.19
Range3.37
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2688617898
Coefficient of variation (CV)0.008939082103
Kurtosis1.331787582
Mean30.07711381
Median Absolute Deviation (MAD)0.15
Skewness-0.4821968351
Sum316591.7
Variance0.07228666201
MonotonicityNot monotonic
2021-12-02T20:43:54.683813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.09230
 
2.2%
30.1222
 
2.1%
30.14218
 
2.1%
30.11213
 
2.0%
30.06200
 
1.9%
30.03196
 
1.9%
30.08189
 
1.8%
30.2189
 
1.8%
30.07189
 
1.8%
30.16187
 
1.8%
Other values (172)8493
80.7%
ValueCountFrequency (%)
28.821
 
< 0.1%
28.922
< 0.1%
28.993
< 0.1%
29.011
 
< 0.1%
29.041
 
< 0.1%
29.061
 
< 0.1%
29.071
 
< 0.1%
29.092
< 0.1%
29.11
 
< 0.1%
29.111
 
< 0.1%
ValueCountFrequency (%)
32.191
 
< 0.1%
31.611
 
< 0.1%
30.861
 
< 0.1%
30.851
 
< 0.1%
30.841
 
< 0.1%
30.822
 
< 0.1%
30.775
< 0.1%
30.763
 
< 0.1%
30.757
0.1%
30.748
0.1%

Pressure_M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct213
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.84318165
Minimum0
Maximum30.68
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:54.813851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.27
Q129.71
median29.88
Q330.04
95-th percentile30.27
Maximum30.68
Range30.68
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.4208023494
Coefficient of variation (CV)0.01410045197
Kurtosis2405.784376
Mean29.84318165
Median Absolute Deviation (MAD)0.17
Skewness-34.41313394
Sum314129.33
Variance0.1770746173
MonotonicityNot monotonic
2021-12-02T20:43:54.935872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.83197
 
1.9%
29.92197
 
1.9%
29.91192
 
1.8%
29.85184
 
1.7%
29.93183
 
1.7%
29.96179
 
1.7%
29.88179
 
1.7%
29.98177
 
1.7%
29.87173
 
1.6%
29.9172
 
1.6%
Other values (203)8693
82.6%
ValueCountFrequency (%)
01
< 0.1%
24.421
< 0.1%
26.951
< 0.1%
28.011
< 0.1%
28.391
< 0.1%
28.431
< 0.1%
28.481
< 0.1%
28.52
< 0.1%
28.511
< 0.1%
28.532
< 0.1%
ValueCountFrequency (%)
30.681
 
< 0.1%
30.651
 
< 0.1%
30.641
 
< 0.1%
30.621
 
< 0.1%
30.612
 
< 0.1%
30.63
< 0.1%
30.591
 
< 0.1%
30.583
< 0.1%
30.575
< 0.1%
30.561
 
< 0.1%

Precipitation_Total
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct226
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1253505605
Minimum0
Maximum6.84
Zeros6528
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:55.063907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.77
Maximum6.84
Range6.84
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.3418088889
Coefficient of variation (CV)2.726823777
Kurtosis48.83930999
Mean0.1253505605
Median Absolute Deviation (MAD)0
Skewness5.419890549
Sum1319.44
Variance0.1168333165
MonotonicityNot monotonic
2021-12-02T20:43:55.302967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06528
62.0%
0.01589
 
5.6%
0.02262
 
2.5%
0.03168
 
1.6%
0.04151
 
1.4%
0.05141
 
1.3%
0.07112
 
1.1%
0.06112
 
1.1%
0.0897
 
0.9%
0.0990
 
0.9%
Other values (216)2276
 
21.6%
ValueCountFrequency (%)
06528
62.0%
0.01589
 
5.6%
0.02262
 
2.5%
0.03168
 
1.6%
0.04151
 
1.4%
0.05141
 
1.3%
0.06112
 
1.1%
0.07112
 
1.1%
0.0897
 
0.9%
0.0990
 
0.9%
ValueCountFrequency (%)
6.841
< 0.1%
5.721
< 0.1%
4.611
< 0.1%
4.411
< 0.1%
4.41
< 0.1%
4.11
< 0.1%
3.921
< 0.1%
3.81
< 0.1%
3.791
< 0.1%
3.351
< 0.1%

Level
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Level_1
4294 
Level_2
3583 
Level_3
1630 
Level_4
1019 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel_2
2nd rowLevel_1
3rd rowLevel_4
4th rowLevel_1
5th rowLevel_1

Common Values

ValueCountFrequency (%)
Level_14294
40.8%
Level_23583
34.0%
Level_31630
 
15.5%
Level_41019
 
9.7%

Length

2021-12-02T20:43:55.437991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:55.504017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
level_14294
40.8%
level_23583
34.0%
level_31630
 
15.5%
level_41019
 
9.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Weather_Type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Sunny
5392 
Light_Rain
1611 
Windy
1136 
Heavy_Rain
981 
Light_Snow
766 
Other values (3)
640 

Length

Max length10
Median length5
Mean length6.841155235
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWindy
2nd rowWindy
3rd rowWindy
4th rowWindy
5th rowWindy

Common Values

ValueCountFrequency (%)
Sunny5392
51.2%
Light_Rain1611
 
15.3%
Windy1136
 
10.8%
Heavy_Rain981
 
9.3%
Light_Snow766
 
7.3%
Heavy_Snow458
 
4.4%
Storm107
 
1.0%
SnowStorm75
 
0.7%

Length

2021-12-02T20:43:55.607036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-02T20:43:55.689059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sunny5392
51.2%
light_rain1611
 
15.3%
windy1136
 
10.8%
heavy_rain981
 
9.3%
light_snow766
 
7.3%
heavy_snow458
 
4.4%
storm107
 
1.0%
snowstorm75
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.396447
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:55.826072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.664342462
Coefficient of variation (CV)0.0008245865
Kurtosis-1.221755861
Mean2018.396447
Median Absolute Deviation (MAD)1
Skewness0.04589071239
Sum21245641
Variance2.77003583
MonotonicityNot monotonic
2021-12-02T20:43:55.917092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20161830
17.4%
20201830
17.4%
20171825
17.3%
20181825
17.3%
20191825
17.3%
20211391
13.2%
ValueCountFrequency (%)
20161830
17.4%
20171825
17.3%
20181825
17.3%
20191825
17.3%
20201830
17.4%
20211391
13.2%
ValueCountFrequency (%)
20211391
13.2%
20201830
17.4%
20191825
17.3%
20181825
17.3%
20171825
17.3%
20161830
17.4%

Month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
May
930 
August
930 
July
930 
March
930 
January
930 
Other values (7)
5876 

Length

Max length9
Median length6
Mean length6.085597568
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
May930
8.8%
August930
8.8%
July930
8.8%
March930
8.8%
January930
8.8%
June900
8.6%
September900
8.6%
April900
8.6%
February850
8.1%
October801
7.6%
Other values (2)1525
14.5%

Length

2021-12-02T20:43:56.024118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may930
8.8%
august930
8.8%
july930
8.8%
march930
8.8%
january930
8.8%
june900
8.6%
september900
8.6%
april900
8.6%
february850
8.1%
october801
7.6%
Other values (2)1525
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Month_Number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.334790044
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:56.119138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.387642765
Coefficient of variation (CV)0.5347679626
Kurtosis-1.160931195
Mean6.334790044
Median Absolute Deviation (MAD)3
Skewness0.04762853165
Sum66680
Variance11.4761235
MonotonicityNot monotonic
2021-12-02T20:43:56.204158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8930
8.8%
1930
8.8%
3930
8.8%
5930
8.8%
7930
8.8%
9900
8.6%
4900
8.6%
6900
8.6%
2850
8.1%
10801
7.6%
Other values (2)1525
14.5%
ValueCountFrequency (%)
1930
8.8%
2850
8.1%
3930
8.8%
4900
8.6%
5930
8.8%
6900
8.6%
7930
8.8%
8930
8.8%
9900
8.6%
10801
7.6%
ValueCountFrequency (%)
12775
7.4%
11750
7.1%
10801
7.6%
9900
8.6%
8930
8.8%
7930
8.8%
6900
8.6%
5930
8.8%
4900
8.6%
3930
8.8%

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.69855596
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2021-12-02T20:43:56.300179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.809089012
Coefficient of variation (CV)0.5611400842
Kurtosis-1.196744085
Mean15.69855596
Median Absolute Deviation (MAD)8
Skewness0.01005092038
Sum165243
Variance77.60004922
MonotonicityNot monotonic
2021-12-02T20:43:56.401201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4349
 
3.3%
1349
 
3.3%
2349
 
3.3%
6349
 
3.3%
3349
 
3.3%
5349
 
3.3%
7347
 
3.3%
12345
 
3.3%
23345
 
3.3%
15345
 
3.3%
Other values (21)7050
67.0%
ValueCountFrequency (%)
1349
3.3%
2349
3.3%
3349
3.3%
4349
3.3%
5349
3.3%
6349
3.3%
7347
3.3%
8345
3.3%
9345
3.3%
10345
3.3%
ValueCountFrequency (%)
31200
1.9%
30315
3.0%
29325
3.1%
28345
3.3%
27345
3.3%
26345
3.3%
25345
3.3%
24345
3.3%
23345
3.3%
22345
3.3%

Date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2107
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
2019-11-28
 
5
2021-09-08
 
5
2020-11-07
 
5
2016-07-06
 
5
2021-05-20
 
5
Other values (2102)
10501 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-01-01
2nd row2016-01-02
3rd row2016-01-03
4th row2016-01-04
5th row2016-01-05

Common Values

ValueCountFrequency (%)
2019-11-285
 
< 0.1%
2021-09-085
 
< 0.1%
2020-11-075
 
< 0.1%
2016-07-065
 
< 0.1%
2021-05-205
 
< 0.1%
2020-05-225
 
< 0.1%
2021-04-235
 
< 0.1%
2018-11-025
 
< 0.1%
2018-11-065
 
< 0.1%
2019-11-245
 
< 0.1%
Other values (2097)10476
99.5%

Length

2021-12-02T20:43:56.515241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-11-285
 
< 0.1%
2018-01-285
 
< 0.1%
2016-12-105
 
< 0.1%
2016-06-285
 
< 0.1%
2019-03-165
 
< 0.1%
2020-07-125
 
< 0.1%
2016-11-065
 
< 0.1%
2021-09-275
 
< 0.1%
2018-12-255
 
< 0.1%
2017-05-125
 
< 0.1%
Other values (2097)10476
99.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-02T20:43:45.790609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:42:58.345215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:00.934925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:03.441473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:05.883044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:08.258563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:10.564688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:12.975244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:15.451804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:17.839326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:20.486134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:23.210745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:25.595270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:28.082851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:30.626421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:33.227997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:35.807578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:38.357835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:40.959603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:43.330065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:45.907653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:42:58.463235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:01.056934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:03.554506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:05.992051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:08.372185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:10.678714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-02T20:43:13.090253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-12-02T20:43:56.628266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-02T20:43:56.966332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-02T20:43:57.322410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-02T20:43:57.622478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-02T20:43:57.810534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-02T20:43:48.460805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-02T20:43:49.699067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0StationlatitudelongitudeBoroughCityStateZipCodeTemperature_MaxTemperature_AvgTemperature_MDewpot_MaxDewpot_AvgDewpot_MHumidity_MaxHumidity_AvgHumidity_MWdspeed_MaxWdspeed_AvgWdspeed_MPressure_MaxPressure_MPrecipitation_TotalLevelWeather_TypeYearMonthMonth_NumberDayDate
00KNYBRONX1440.8616-73.8809BronxBotanical_GardenNY1045841.238.133.926.921.916.96051.04528.411.31.330.1029.960.0Level_2Windy2016January112016-01-01
11KNYBRONX1440.8616-73.8809BronxBotanical_GardenNY1045839.435.232.419.317.514.05648.04225.310.10.030.1129.950.0Level_1Windy2016January122016-01-02
22KNYBRONX1440.8616-73.8809BronxBotanical_GardenNY1045844.738.634.523.521.119.56049.03628.410.81.629.9729.780.0Level_4Windy2016January132016-01-03
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